Change point detection technologies are used to recognize or estimate locations of change points, positions at which a trend or feature of data changes in a data series. Change point detection can be used to distinguish different patterns within data, provide a better understanding of the data, and facilitate better data-based decisions and predictions. Existing change point detection techniques exclusively use data from a target time series. Such techniques are limited in circumstances in which the data in the target time series is sparse and/or noisy because the scarcity and noise of the target time series data makes it difficult to accurately detect change points. Existing change point detection techniques are also typically run on large data sets in their entireties, i.e., on all of the data in the data set in a single instance, which requires significant processing time and resources. Such techniques are impractical in circumstances in which new target time series data is received on an on-going basis.
These and other drawbacks of existing change point detection techniques make them ill-suited for use in the context of online advertising systems, which can involve sparse, noisy target series data that is received on an on-going bases. Online advertising optimization systems usually rely on historical time series data to predict future advertisement performance. However, change points are very common in online advertising. Change points in a revenue per click time series, for example, can be caused by a new product release, a price discount occurring, changes in a landing web page, changes in a competitors' strategy, etc. The usefulness of the revenue per click time series data could be greatly improved if change points in the data could be accurately detected. For example, once change points are detected, strategies such as dropping or heavily decaying old data before the change points could be used to improve the accuracy of the predictive models. While there would be benefits to accurately detecting change points in online advertising systems, existing change point detection techniques do not provide sufficiently accurate, timely, and efficient change point detection for use with the sparse, noisy, on-going target time series data of those systems.